Question

A large direct health and insurance medical provider needed an enterprise information management system to enable enterprise-wide information management and to support the effective use of data for critical cross-functional decision making. In addition, the company needed to resolve issues related to data redundancy, inconsistency, and unnecessary expenditure. The company faced several information challenges: The company data resided in multiple locations, the data were developed for department-specific use, and there was limited enterprise access. Also, data definitions were created by individual departments and were not standardized, and data were being managed by multiple departments within the company.

• Given the situation, What solution would you propose for this company?
• Discuss the role that data modelling would play in a project to solve this problem.
• Support or Oppose the above statements with arguments

Answer 1

Information about the same patient will be generated and entered into the database through various tools in own proprietary data formats are employed. In this case, if any flawed user input are entered and imperfect database design leads to data inconsistency and redundancy.

In order to preclude data redundancy, inconsistency problems and less expenditure to create effective database structures before they are deployed. But in some cases, database which does not have effective data structures that can suffer from these problems, a process of database normalization should be implemented. The purpose of database normalization is to re-modify tables such a way that the relations among them are logical, so that database is scalable without any anomalies and avoid data redundancy, inconsistency problems and less expenditure. It is recommendable to design the database in the OLTP format which are highly normalized which avoids data duplication errors.

In health and insurance,a central semantic store approach can be deployed which concentrates on logging as well as storing all the rules employed by the database integration process in a single centralized repository. The reason for this approach is that data sources are updated and new ones which that included do not fall outside data integration rules.